class_docstr: Inherits time series methods from TSM class.
  
  **** DYNAMIC REGRESSION MODEL ****
  
  Parameters
  ----------
  
  formula : string
      patsy string describing the regression
  
  data : pd.DataFrame
      Field to specify the data that will be used
docstr: Creates the structure of the model
  
  Parameters
  ----------
  data : np.array
      Contains the time series
  
  beta : np.array
      Contains untransformed starting values for latent variables
  
  Returns
  ----------
  a,P,K,F,v : np.array
      Filted states, filtered variances, Kalman gains, F matrix, residuals
docstr_tok: ["Creates","the","structure","of","the","model","Parameters","--","--","--","--","--","data",":","np.array","Contains","the","time","series","beta",":","np.array","Contains","untransformed","starting","values","for","latent","variables","Returns","--","--","--","--","--","a",",","P",",","K",",","F",",","v",":","np.array","Filted","states",",","filtered","variances",",","Kalman","gains",",","F","matrix",",","residuals"]
code: def _model(self, data, beta):
      """Creates the structure of the model
  
      Parameters
      ----------
      data : np.array
          Contains the time series
  
      beta : np.array
          Contains untransformed starting values for latent variables
  
      Returns
      ----------
      a,P,K,F,v : np.array
          Filted states, filtered variances, Kalman gains, F matrix, residuals
      """
  
      T, Z, R, Q, H = self._ss_matrices(beta)
  
      return dl_univariate_kalman(data, Z, H, T, Q, R, 0.0)
  
imports: import copy
  import sys
  import numpy
  import pandas
  import scipy.stats
  from patsy import dmatrices, dmatrix, demo_data
  from ..None import families
  from ..None import tsm
  from ..None import data_check
  from .kalman import *
label_desc: Use image node_id 4 for calling the DynReg obj's underlying member method code with example usage: obj._model(data, beta) and returns: dl_univariate_kalman